Hi, I'm

Benjamin Scarbrough

Building secure, reliable systems at scale. Leading the teams that keep them running.

Software Engineer at Microsoft, operating secure production cloud systems across public and sovereign environments: reliability, automation, and incident response. I take on high-stakes work and turn it into services that hold up in production.

As Director of Operations at TEDxAnchorage, I serve on a 10-person executive team leading the volunteer crew that delivers Alaska's premier TEDx event.

About Me

Benjamin Scarbrough

My background runs through aerospace defense, cloud reliability, and operations leadership. It starts with mission-critical software at Lockheed Martin Space, where rigor and correctness were the baseline, and continues as a Software Engineer at Microsoft, operating production services at scale under real reliability, security, and compliance demands.

I lead cross-functional delivery across engineering, operations, and leadership, carry compliance and audit readiness, and own outcomes from design to production. I work best in secure, high-stakes environments where reliability and trust are the standard.

Technical Expertise

Site Reliability Engineering
Cloud Infrastructure
Azure
Incident Response
Monitoring & Observability
CI/CD
Infrastructure as Code
Automation
Distributed Systems
Docker
Python
C#
Java
TypeScript

Core Competencies

Cross-functional Team Leadership
Stakeholder Management
Technical Mentorship
Operations Management
Bilingual (English & Spanish)

Professional Experience

Microsoft

TEDxAnchorage

Lockheed Martin Space

Self-Employed

PopOver

1 / 5

Software Engineer

Microsoft

November 2022 - Present

Responsible for reliability, performance, and deployment of mission-critical cloud infrastructure across both public and isolated production cloud environments.

  • Share weekly primary/backup on-call across a 50+ service production portfolio spanning public and sovereign cloud environments, with direct squad responsibility for a subset across web-application, backend, and specialized platform tiers.
  • Achieved FedRAMP audit readiness across multiple annual cycles, collecting full evidence in ~2 days versus the weeks typical of comparable environments, by preparing the evidence process ahead of each request and documenting it as the team's standing procedure. Supports sales into compliance-restricted public sector markets.
  • Led an end-to-end SLA dataset recovery under production pressure that prevented tens of millions in financial and compliance impact.
  • Drove incident auto-mitigation from 0% to a peak of ~95% over 9 months in a sovereign cloud environment, with auto-resolution rising from 0% to a 39-65% band, by enabling auto-mitigation rules across the service monitors and establishing the certificate chain that let the incident monitoring service mitigate tickets once conditions returned healthy, replacing manual adjustment of every ticket.
  • Remediated two pre-authentication vulnerabilities (SSRF and forged-JWT) in an internal validation dashboard prior to production rollout; served as team Security Champion driving org-wide vulnerability burndown.
  • Cut manual certificate rotation ~90% by migrating three service families to managed identities, returning ~30 engineer-hours/month; reduced manual configuration work ~70% via a centralized config service.
  • Built six operational tooling packages for on-call workflows: privileged-access toolkit, cloud operations toolkit, service validation framework, validation dashboard, alert-generation module, and an AI-assisted synthetic-job generator.
  • Drove ~$1.5M/yr recurring cost savings by re-tiering storage across eight services with no loss of compliance posture.
  • Built net-new AFD/CDN monitoring for a 50+ service portfolio where the resource type had no third-party monitoring support, using a Log Analytics workspace and 36 Azure Monitor alert rules on latency, error conditions, and absence of traffic, incident-routed and hardened against schema drift.
  • Authored a reusable deployment-migration playbook adopted by multiple partner teams, preventing recurrence of a prior production CDN outage.
  • Eliminated manual deployment toil for five service teams via automated pipelines, reusable tooling, and three shipped AI (Copilot) plugins.

Stories

The reasoning behind a couple of the numbers above.

Monitoring a resource the platform would not monitor

Microsoft, sovereign cloud

36Net-new alert rules, AFD/CDN across 50+ services
The problem

First-party monitoring existed, but the resource type had no support for third-party monitoring, and nothing covered the AFD/CDN layer sitting in front of 50+ services the way ours was set up. Not a gap in what had been configured, but a gap in what the platform made possible. Nobody was addressing it.

The approach

Assemble the monitoring plane out of Azure primitives rather than wait for a product: a Log Analytics workspace, and 36 Azure Monitor alert rules tied directly to the resources, covering latency, error conditions, and absence of traffic. That last one matters most. A CDN's worst failure is silence, and anything alerting only on the errors it receives never sees it.

The outcome

It has already caught real drops that nothing else would have seen. Modest by design: the point was never volume of alerting, it was that a whole layer of the estate went from unobservable to observable.

Nobody should close a ticket a machine already fixed

Microsoft, sovereign cloud

0% → ~95%Incident auto-mitigation, 9 months
The problem

Conditions cleared on their own, and every ticket still had to be adjusted by hand. Auto-mitigation sat at 0% across the portfolio, not because nothing recovered, but because the incident monitoring service had no authorized way to say so.

The approach

Auto-mitigation rules on every service monitor, switched on rather than left staged, and the certificate chain that let the incident monitoring service authenticate to the incident service and mitigate a ticket once the underlying condition was healthy again. The rules were the easy half; without that trust chain they were inert.

The outcome

A peak of ~95% over 9 months, with auto-resolution rising into a 39-65% band. The band is wide on purpose: full auto-resolution only ever went to failure modes with a known-safe recovery, and the rest still stop at a human. A machine that resolves an incident it does not understand is not reliability, it is a slower outage.

Ready before the request came

Microsoft, FedRAMP cycles

~2 daysFull evidence collection, versus weeks
The problem

Audit cycles usually start reactive. The request lands, and then a scramble begins to reconstruct evidence nobody was maintaining, because getting ahead of it is not anybody in particular's job.

The approach

Know what the cycle will ask for and have it standing before it is asked, unprompted. It was inside my scope and simply was not being addressed. Writing the process down while doing it mattered as much as the preparation. It is the team's documentation now, so the next cycle starts from a procedure rather than from one person's memory.

The outcome

Full collection came down to ~2 days across multiple annual cycles. Audit readiness is what makes a service sellable into compliance-restricted public sector markets, so the difference between weeks and days is the difference between a market being open or closed.

Feedback

Paraphrased from written performance reviews.

Completed a federal compliance audit in a fraction of the time taken by any comparable environment, crediting deep working knowledge of the underlying services and platforms.
Engineering Manager, Microsoft
Received positive feedback across the organization on the engineering documentation rewrite, noting it as a model other teams could follow.
Engineering Manager, Microsoft
Described the work as forward looking, reducing the overall effort the team would need to meet upcoming deadlines.
Engineering Manager, Microsoft

Projects

Other Projects

Source not public
Shrike

A quant trading research engine built as a multi-repo platform: a shared analysis core (scoring engine, guardrails, LLM provider abstraction) feeding a user-facing analysis app and an autonomous paper-trading research project. Pulls from 49+ data sources (FRED, BLS, SEC EDGAR, Finnhub, and more) with heavy contract and serialization-drift discipline enforced by cross-repo tests.

PythonFastAPIAzure Key VaultLLMQuant Analysis
TailClaw

An infrastructure tool that deploys a hardened AI agent gateway on Azure, reachable only over a Tailscale mesh VPN with no public IP and a deny-all-inbound firewall. Handles OS hardening (UFW, fail2ban, key-only SSH, unattended patches), loopback-only services, automated backups, and health checks.

AzureBashTailscaleLinuxSecurity Hardening
Source not public
Clausely

A neutral AI legal-document summarizer that explains contracts and terms of service in plain language, strictly framed as summaries rather than advice. Runs on Azure Container Apps with a daily LLM spend cap and HMAC-signed shareable results.

Next.jsTypeScriptAzure Container AppsOpenAI
Source not public
PopOver

Brainstormed, created, and tested an iOS application designed to facilitate work relationships between college students and their community members, providing every college student with an opportunity for financial liberation.

SwiftFirebaseSQLHTMLJavaScript
Source not public
UPS Safe

Designed, created, and stress-tested a JavaScript web application responsible for ensuring that in the event of a campus-wide emergency all university students and faculty can check in with campus security while both on and off-campus.

Node.jsExpress.jsJavaScriptMySQLAWSBlazeMeter
Source not public
bscarbrough.com

Designed and developed this modern portfolio website using Angular standalone components with custom scroll animations, responsive design, and Firebase hosting. Features TypeScript interfaces, Material Design integration, and ng-bootstrap carousel.

AngularTypeScriptFirebaseMaterial DesignCSS
Schedulizer

Designed and created a scheduling application to improve the current way that students at the University of Puget Sound to view and schedule their classes for the semester.

JavaScriptHTMLCSSHeroku
AI Image Recognition

Developed a machine learning system using convolutional neural networks for multi-object detection and classification in images.

PythonTensorFlowDeep Learning
Location-Based Business Finder

Full-stack web application for discovering and rating local businesses with user reviews and geolocation features.

MongoDBExpress.jsAngularNode.js

Education

University of Puget Sound

Bachelor of Science, Computer Science

2016 - 2019

Minor in Mathematics & Spanish

Montana State University, Bozeman

Pre-Medicine/Pre-Medical Studies

2015 - 2016

Get In Touch

I'm always interested in new opportunities, collaborations, and connecting with other engineers and technologists.

If you have a question, a project to discuss, or a role in mind, reach out and I'll get back to you.